Systems and methods for facilitating communication between a user and a service provider

ABSTRACT

Systems and methods are disclosed for generating a dynamic customized script to facilitate communication between a user and a service provider. The method includes receiving, via an interactive voice response (IVR) system, a request from a mobile device associated with at least one user. The contextual information associated with at least one user is processed, in real-time, based, at least in part, on the request. A dynamic customized script specific to the request is generated, in real-time, based, at least in part, on the processing of the contextual information. The request is routed, via the IVR system, to an agent from a pool of agents of the service provider. A presentation of the dynamic customized script is generated in a user interface of a device associated with the agent, wherein the dynamic customized script is step-by-step guidance to the agent for handling the request of at least one user.

TECHNICAL FIELD

The present disclosure relates generally to the field of computertelephony integration (CTI) technology and Interactive Voice Response(IVR) technology. More particularly, the present disclosure relates tothe processing of a communication session in a CTI system and/or an IVRsystem to facilitate communication between a caller and a serviceprovider.

BACKGROUND

Organizations route callers using an IVR system that providescomputer-generated voice messages to guide the caller, e.g., a callermay hear an audio message requesting credit card information, whereuponthe caller may provide the credit card information by voice input or byentering the information via the keypad of the mobile device. The IVRsystem may ask a series of additional questions to connect the caller toa suitable agent. Typically, this process is time-consuming because thecaller has to listen and follow instructions to get the desired responseor a function performed. This process is also laborious and error-prone,e.g., if the caller provides an incorrect input, the process may have tobe repeated.

Furthermore, while transitioning the call to an agent the context of thecall may be lost, and the caller may need to provide personalinformation. It is frustrating for the caller to repeat the informationthey had previously provided. Moreover, when the context of the call islost, the system is unable to provide required documentation to theagents to address the queries of the callers. Hence the agents may relyon their experience to handle the situation with minimal support fromthe system. This defeats the intent of the IVR system in easing customerservice for callers.

Accordingly, there is a need for systems and methods for streamlinedintegration of the IVR systems to facilitate interaction between acaller and the service provider. Improving the efficiency andeffectiveness of agent-caller interactions may result in greatercustomer satisfaction, reduced operational costs, and efficient businessprocesses.

SUMMARY OF THE DISCLOSURE

According to certain aspects of the disclosure, systems and methods aredisclosed for providing configuration parameters in a streamlined mannerso that developers can construct end-to-end solutions in an automatedmanner.

In one embodiment, a method is disclosed for facilitating interactionbetween at least one user and a service provider. The method includes:receiving, via an interactive voice response (IVR) system, a requestfrom a mobile device associated with the at least one user; processing,in real-time, contextual information associated with the at least oneuser based, at least in part, on the request; generating, in real-time,a dynamic customized script specific to the request based, at least inpart, on the processing of the contextual information; routing, via theIVR system, the request to an agent from a pool of agents of the serviceprovider; and generating a presentation of the dynamic customized scriptin a user interface of a device associated with the agent, wherein thedynamic customized script is a step-by-step guidance to the agent forhandling the request of the at least one user.

In one embodiment, an apparatus comprises at least one processor, and atleast one memory including computer program code for one or morecomputer programs, the at least one memory and the computer program codeconfigured to, with the at least one processor, cause, at least in part,the apparatus to facilitate interaction between at least one user and aservice provider. The apparatus causes: receive, via an interactivevoice response (IVR) system, a request from a mobile device associatedwith the at least one user; process, in real-time, contextualinformation associated with the at least one user based, at least inpart, on the request; generate, in real-time, a dynamic customizedscript specific to the request based, at least in part, on theprocessing of the contextual information; route, via the IVR system, therequest to an agent from a pool of agents of the service provider; andgenerate a presentation of the dynamic customized script in a userinterface of a device associated with the agent, wherein the dynamiccustomized script is a step-by-step guidance to the agent for handlingthe request of the at least one user.

In accordance with another embodiment, a non-transitorycomputer-readable storage medium having stored thereon one or moreprogram instructions which, when executed by one or more processors,cause, at least in part, an apparatus to facilitate interaction betweenat least one user and a service provider. The apparatus causes:receiving, by via interactive voice response (IVR) system, a requestfrom a mobile device associated with the at least one user; processing,in real-time, contextual information associated with the at least oneuser based, at least in part, on the request; generating, in real-time,a dynamic customized script specific to the request based, at least inpart, on the processing of the contextual information; routing, via theIVR system, the request to an agent from a pool of agents of the serviceprovider; and generating a presentation of the dynamic customized scriptin a user interface of a device associated with the agent, wherein thedynamic customized script is a step-by-step guidance to the agent forhandling the request of the at least one user.

Additional objects and advantages of the disclosed embodiments will beset forth in part in the description that follows, and in part will beapparent from the description, or may be learned by practice of thedisclosed embodiments. The objects and advantages on the disclosedembodiments will be realized and attained by means of the elements andcombinations particularly pointed out in the appended claims.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory onlyand are not restrictive of the detailed embodiments, as claimed.

It may be understood that both the foregoing general description and thefollowing detailed description are exemplary and explanatory only andare not restrictive of the invention, as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate various exemplary embodiments andtogether with the description, serve to explain the principles of thedisclosed embodiments.

FIG. 1 is a diagram of a system capable of generating a dynamiccustomized script to facilitate communication between a user and aservice provider, according to one example embodiment;

FIG. 2 is a diagram of the components of integration platform 109,according to one example embodiment;

FIG. 3 is a flowchart of a process for real-time processing ofcontextual information of one or more callers to generate dynamiccustomized scripts, according to one embodiment;

FIG. 4 is a flowchart of a process for updating the dynamic customizedscript, according to one embodiment;

FIG. 5 is a flowchart of a process for determining authenticationmechanisms for a user, according to one embodiment;

FIG. 6 is a flowchart of a process for generating a dynamic customizedscript based on the current activity of the user, according to oneembodiment;

FIG. 7 is a flowchart of a process for presenting a dynamic customizedscript, according to one embodiment;

FIG. 8 is a flowchart of a process for predicting the intent of a call,according to one embodiment;

FIGS. 9A-C illustrate agent interface screens, according to variousexample embodiments;

FIG. 10 is a flowchart for integration of an IVR system and a CTI systemdeployed within a contact center by integration platform 109, accordingto one example embodiment;

FIG. 11 shows an example machine learning training flow chart; and

FIG. 12 illustrates an implementation of a general computer system thatmay execute techniques presented herein.

DETAILED DESCRIPTION OF EMBODIMENTS

While principles of the present disclosure are described herein withreference to illustrative embodiments for particular applications, itshould be understood that the disclosure is not limited thereto. Thosehaving ordinary skill in the art and access to the teachings providedherein will recognize additional modifications, applications,embodiments, and substitution of equivalents all fall within the scopeof the embodiments described herein. Accordingly, the invention is notto be considered as limited by the foregoing description.

Calls are typically routed to contact center agents from the interactivevoice response (IVR) and computer telephony integration (CTI) systems.These IVR and/or CTI systems may capture additional context pertainingto a call, however, in most cases, the context is lost. As a result, thecontact center agent may be required to authenticate the caller all overagain, resulting in caller frustration. Furthermore, there may not be acentralized knowledge base with detailed steps and instructions, andagents may be required to switch between multiple applications whileprocessing the calls, leading to performance inefficiency.

Needless to mention, call flows are independent of the underlyingsystems, and there is limited context regarding the calls when agentsfirst get connected with the caller. The system is also unable toprovide required documentation to the agents because of this limitedcontext requiring manual effort or reliance on agent expertise toaddress the queries of the callers. Furthermore, significant turnoverwithin call center operations requires new/novice call center agents tolearn quickly, but the challenges outlined herein and the steep learningcurve around the processing of actions and transactions during the callhave a direct bearing on the ability of new agents to be effectivequickly.

As shown, the process is inefficient. Firstly, call duration may beexcessively high, e.g., at the frontend the callers are repeatedlysubmitting data and at the backend the agents are working with multiplesystems. This significantly impacts service providers especially theones that outsource call center operations. Secondly, the waiting periodfor the callers may be extended, resulting in lower customersatisfaction. Thirdly, there may be an increase in agent errors as theyare working with multiple systems.

The existing solutions require a lot of manual effort, whether increating context regarding a call, accessing the knowledgebase for callflows, or simply needing to work with multiple applications, e.g., somedesktop-based, some web-based, in order to process a call. As a result,service providers are continually challenged to optimize the operationof their contact centers to manage cost, reduce call durations, andenhance customer experience. Furthermore, data spans multiple sourcesand sub-types, but must be presented in a single user experience (UX) toprevent agents from switching between multiple applications during acall. Hence, service providers are technically challenged to streamlinethe integration with the IVR systems, and generate the call-context datain a simple user interface of the agent.

FIG. 1 is a diagram of a system capable of generating a dynamiccustomized script to facilitate communication between a user and aservice provider, according to one example embodiment. FIG. 1 introducesa capability to implement modern communication and data processingcapabilities into existing methods and systems for generating a dynamiccustomized script to facilitate communication between a user and aservice provider. FIG. 1 , an example architecture of one or moreexample embodiments of the present invention, includes system 100 thatcomprises user equipment (UE) 101 a-101 n (collectively referred to asUE 101) that may include or be associated with applications 103 a-103 n(collectively referred to as applications 103) and sensors 105 a-105 n(collectively referred to as sensors 105). In one embodiment,integration platform 109 has connectivity to computer telephonyintegration (CTI) 111 and interactive voice response (IVR) system 113,and via a communication network 107, e.g., a wireless communicationnetwork.

In one embodiment, system 100, enables service providers to have astreamlined solution for contact centers, which enhances the experiencefor the contact center agent, reduces the duration of the call, andleads to a better experience for the caller. In another embodiment,system 100 enables businesses to optimize their contact centeroperations by reducing call duration and hence their overall costfootprint. This has a direct bearing on overall operational costs,requiring less hands-on-deck to process calls, improvedmargins/productivity, and enhanced customer experience.

As shown in FIG. 1 , system 100 comprises UE 101. In one embodiment, UE101 may include, but is not restricted to, any type of a mobileterminal, wireless terminal, fixed terminal, a landline, or portableterminal. Examples of UE 101, may include, but are not restricted to, amobile handset, a wireless communication device, a station, a unit, adevice, a multimedia computer, a multimedia tablet, an Internet node, acommunicator, a desktop computer, a laptop computer, a notebookcomputer, a netbook computer, a tablet computer, a PersonalCommunication System (PCS) device, a personal navigation device, aPersonal Digital Assistant (PDA), a digital camera/camcorder, aninfotainment system, a dashboard computer, a television device, or anycombination thereof, including the accessories and peripherals of thesedevices, or any combination thereof. In addition, UE 101 may facilitatevarious input means for receiving and generating information, including,but not restricted to, a touch screen capability, a keyboard, and keypaddata entry, a voice-based input mechanism, and the like. Any known andfuture implementations of UE 101 may also be applicable.

UE 101 includes applications 103. Further, applications 103 may includevarious applications such as, but not restricted to, contentprovisioning application, networking application, camera/imagingapplication, multimedia application, location-based application, mediaplayer applications, social networking applications, and the like. Inone embodiment, one of the applications 103 at UE 101 may act as aclient for integration platform 109 and perform one or more functionsassociated with the functions of integration platform 109 by interactingwith integration platform 109 over the communication network 107.

By way of example, sensor 105 may be any type of sensor. In oneembodiment, the sensors 105 may include, for example, a networkdetection sensor for detecting wireless signals or receivers fordifferent short-range communications (e.g., Bluetooth, Wi-Fi, Li-Fi,near field communication (NFC), etc.), temporal information sensors, aglobal positioning sensor for gathering location data, a camera/imagingsensor for gathering image data, an audio recorder for gathering audiodata, and the like. Any known and future implementations of sensor 105may also be applicable.

Further, various elements of the system 100 may communicate with eachother through a communication network 107. The communication network 107of system 100 includes one or more networks such as a data network, awireless network, a telephony network, or any combination thereof. It iscontemplated that the data network may be any local area network (LAN),metropolitan area network (MAN), wide area network (WAN), a public datanetwork (e.g., the Internet), short range wireless network, or any othersuitable packet-switched network, such as a commercially owned,proprietary packet-switched network, e.g., a proprietary cable orfiber-optic network, and the like, or any combination thereof. Inaddition, the wireless network may be, for example, a cellular networkand may employ various technologies including 5G (5th Generation), 4G,3G, 2G, Long Term Evolution (LTE), enhanced data rates for globalevolution (EDGE), general packet radio service (GPRS), global system formobile communications (GSM), Internet protocol multimedia subsystem(IMS), universal mobile telecommunications system (UMTS), etc., as wellas any other suitable wireless medium, e.g., worldwide interoperabilityfor microwave access (WiMAX), code division multiple access (CDMA),wideband code division multiple access (WCDMA), wireless fidelity(Wi-Fi), wireless LAN (WLAN), Bluetooth®, Internet Protocol (IP) datacasting, satellite, mobile ad-hoc network (MANET), and the like, or anycombination thereof.

In one embodiment, integration platform 109 may be a platform withmultiple interconnected components. Integration platform 109 may includeone or more servers, intelligent networking devices, computing devices,components, and corresponding software for generating a dynamiccustomized script to facilitate communication between a user and aservice provider. In addition, it is noted that integration platform 109may be a separate entity of the system 100, a part of the one or moreservices 121 a-121 j (collectively referred to as services 121) of theservices platform 119, or the UE 101. Any known or still developingmethods, techniques, or processes for generating a dynamic customizedscript to facilitate communication between a user and a service providermay be employed by integration platform 109.

In one embodiment, integration platform 109 may streamline call handlingfor contact center agents within a single application. In one exampleembodiment, integration platform 109 may integrate with the IVR and CTIsystems to determine call context, e.g., caller details (such as cardinformation) and call purpose (such as charge dispute), before the agentspeaks with the caller. In another embodiment, integration platform 109may validate a user based, at least in part, on user credentials, deviceidentifiers, biometrics data, credit card information, or a combinationthereof. Integration platform 109 may process, in real-time, contextualinformation, historical information, or a combination thereof associatedwith the user to gather required information for the call and maygenerate a dynamic script that defines a step-by-step process forhandling the call. Integration platform 109 may update the script inreal-time based, at least in part, on the varying context of the call,changes in service level agreement (SLA) that may have a bearing on thehandling of calls, or a combination thereof. In a further embodiment,integration platform 109 may generate a unified display 123 to presentuser information and the dynamic script. Since the user information andthe dynamic script are presented in a single interface of UE 101 byabstracting the behavior of the underlying systems, the agents do nothave to switch between a plurality of applications while interactingwith a caller.

It is noted that the disclosed subject matter relates to telephone callsto an IVR of a service provider, yet the disclosed subject matter,possibly with variations, may be applied to other media such aswebpages, emails, SMS, and so forth. In one example embodiment, usersmay access the website of the service provider and may authenticatethemselves via the website by entering credential information and/ordevice identifiers. Integration platform 109 may process, in real-time,contextual information of the users and may determine the users' need tobe connected with an agent. Integration platform 109 may notify the userto call the contact center, whereupon integration platform 109 mayintegrate with the IVR and CTI systems to route the call to anappropriate agent. In one embodiment, integration platform 109 and IVRsystem 113 may use speech recognition algorithms to recognize the speechof the caller and then perform actions corresponding to the speech ofthe caller. In one embodiment, integration platform 109 may implement aninitialization algorithm that provides the initial data that can be usedby an intelligent IVR system to provide automatic IVR authentication andintention verification.

Computer telephony integration (CTI) system 111 may allow computers andtelephones to interact and exchange information. In one embodiment, CTIsystem 111 may provide programmatic methods for executing CTI methods,e.g., call control operations such as answering a call, making a call,and transferring a call. In another embodiment, CTI system 111 mayauthenticate callers by screening the telephone number of the calleragainst a database to match it to a customer record and populate thatdata instantaneously in a user interface of a device associated with acall center agent. In one embodiment, CTI system 111 or at least itsfunctionality may be incorporated into IVR system 113.

In one embodiment, IVR system 113 may be coupled to CTI system 111 forrouting incoming calls from the callers over to at least one IVR system113 or to at least one of the plurality of agent terminals. IVR system113 may be an automated call processing system that providesself-service interactions to callers. In one embodiment, IVR system 113may provide pre-recorded audible responses triggered for playback inresponse to user input. In another embodiment, IVR system 113 mayinclude voice recognition technology that processes natural languageinput from callers to properly service the request of the callers, e.g.,routing the call to an agent when requested by the caller or whendetected as necessary. In one example embodiment, when a caller dialsinto IVR system 113, IVR system 113 may determine identification andintention. Identification is focused on authenticating the user'sidentity, e.g., IVR system 113 may prompt the caller to enterinformation that validates or authenticates the caller to use the IVRsystem. After the user is authenticated, the caller may select from aseries of options to indicate their intention, i.e., the purpose of thecall. IVR system 113 may then instruct CTI system 111 to transfer thecall from IVR system 113 to at least one of the plurality of agentterminals.

In one embodiment, database 115 may be any type of database, such asrelational, hierarchical, object-oriented, and/or the like, wherein dataare organized in any suitable manner, including as data tables or lookuptables. In one embodiment, database 115 may store and manage multipletypes of information that can provide means for aiding in the contentprovisioning and sharing process. In an embodiment, database 115 mayinclude a machine-learning based training database with pre-definedmapping defining a relationship between various input parameters andoutput parameters based on various statistical methods. In anembodiment, the training database may include machine-learningalgorithms to learn mappings between input parameters related to theuser such as but not limited to user's online activity, historical userinformation, etc. In an embodiment, the training database is routinelyupdated and/or supplemented based on machine learning methods.

The content providers 117 a-117 k (collectively referred to as contentprovider 117) may provide content to UE 101, integration platform 109,and services 121 of the services platform 119. The content provided maybe any type of content, such as, image content, textual content, audiocontent, video content, etc. In one embodiment, the content provider 117may provide content that may supplement the content of the applications103, the sensors 105, or a combination thereof. In one embodiment, thecontent provider 117 may provide or supplement the notificationservices/application, social networking services/application, content(e.g., audio, video, images, etc.) provisioning services/application,application services/application, storage services/application,contextual information determination services/application,location-based services/application, or any combination thereof. In oneembodiment, the content provider 117 may also store content associatedwith UE 101, integration platform 109, and services 121 of servicesplatform 119. In another embodiment, the content provider 117 may manageaccess to a central repository of data, and offer a consistent, standardinterface to data.

The services platform 119 may include any type of service. By way ofexample, services platform 119 may include content (e.g., audio, video,images, etc.) provisioning services/application, notificationservices/application, contextual information determinationservices/application, notification services/application, storageservices/application, social networking services/application,information-based services, etc. In one embodiment, services platform119 may interact with UE 101, integration platform 109, and contentprovider 117 to supplement or aid in the processing of the contentinformation. In one embodiment, the services platform 119 may beimplemented or embedded in integration platform 109 or in its functions.

By way of example, services 121 may be an online service that reflectsthe interests and/or activities of users. Services 121 allow users toshare contact information, location information, activities information,contextual information, historical user information, and interestswithin their individual networks, and provides for data portability.Services 121 may additionally assist in providing integration platform109, CTI system 111, and IVR system 113 with activity information of atleast one user, user profile information, and a variety of additionalinformation.

By way of example, UE 101, integration platform 109 communicate witheach other and other components of the communication network 107 usingwell-known, new or still developing protocols. In this context, aprotocol includes a set of rules defining how the network nodes withinthe communication network 107 interact with each other based oninformation sent over the communication links. The protocols areeffective at different layers of operation within each node, fromgenerating and receiving physical signals of various types, to selectinga link for transferring those signals, to the format of informationindicated by those signals, to identifying which software applicationexecuting on a computer system sends or receives the information. Theconceptually different layers of protocols for exchanging informationover a network are described in the Open Systems Interconnection (OSI)Reference Model.

Communications between the network nodes are typically effected byexchanging discrete packets of data. Each packet typically comprises (1)header information associated with a particular protocol, and (2)payload information that follows the header information and containsinformation that may be processed independently of that particularprotocol. In some protocols, the packet includes (3) trailer informationfollowing the payload and indicating the end of the payload information.The header includes information such as the source of the packet, itsdestination, the length of the payload, and other properties used by theprotocol. Often, the data in the payload for the particular protocolincludes a header and payload for a different protocol associated with adifferent, higher layer of the OSI Reference Model. The header for aparticular protocol typically indicates a type for the next protocolcontained in its payload. The higher layer protocol is said to beencapsulated in the lower layer protocol. The headers included in apacket traversing multiple heterogeneous networks, such as the Internet,typically include a physical (layer 1) header, a data-link (layer 2)header, an internetwork (layer 3) header and a transport (layer 4)header, and various application (layer 5, layer 6 and layer 7) headersas defined by the OSI Reference Model.

FIG. 2 is a diagram of the components of integration platform 109,according to one example embodiment. By way of example, integrationplatform 109 includes one or more components for generating a dynamiccustomized script to facilitate communication between a user and aservice provider. It is contemplated that the functions of thesecomponents may be combined in one or more components or performed byother components of equivalent functionality. In one embodiment,integration platform 109 comprises data collection module 201,authentication module 203, data processing module 205, training module207, machine learning module 209, and user interface module 211, or anycombination thereof.

In one embodiment, data collection module 201 may automatically collectrelevant data associated with users, e.g., registered callers ornon-registered callers, through various data collection techniques. Forexample, data collection module 201 may use a web-crawling component toaccess various databases or other information sources to determine theonline activities information of the users. Data collection module 201may be programmed to collect, in real-time, historical information,contextual information, or a combination thereof pertaining to theusers, so that identification, analysis, response, monitoring, andcontrol may be performed using the most recent data. In one exampleembodiment, data collection module 201 may include various softwareapplications, e.g., data mining applications in Extended Meta Language(XML), that automatically search for and return relevant informationpertaining to the users. In one embodiment, data collection module 201may parse and arrange the data into a common format that can be easilyprocessed by other modules and platforms.

In one embodiment, authentication module 203 may authenticate the users,e.g., registered callers or non-registered callers, and their devicesfor interaction with integration platform 109. In one embodiment,authentication may include receiving and validating a login name and/oruser identification value as provided or established for a particularuser during a registration process with the service provider. In oneembodiment, the authentication procedure may also be performed throughthe automated association of database 115 with an IP address, a carrierdetection signal of a user device, mobile directory number (MDN),subscriber identity module (SIM) (e.g., of a SIM card), radiofrequencyidentifier (RFID) tag or other identifiers. In one example embodiment,authentication module 203 may perform multiple authentications of a userbased, at least in part, on historical user information. In anotherexample embodiment, once a caller is fully authenticated the first time,authentication module 203 may automatically match the profileinformation, device identifiers, and/or biometric data, e.g., voicesample, to authenticate the user the second time. Authentication may begranted based, at least in part, on the confidence level of the match.It is noted that the authentication may also include a first-timeregistration procedure for establishing one or more profile settings.

In one embodiment, data processing module 205 may process data collectedby data collection module 201 to determine context informationassociated with the users, device information associated with the users,payment vehicles associated with the users, or a combination thereof. Byway of example, the context information may include computingenvironment, data environment, online activities, historicalinformation, preference information, spending patterns, locationinformation, or a combination thereof associated with the users. In oneexample embodiment, data processing module 205 may analyze the contextinformation to determine that a user regularly purchases goods andservices from an online shopping portal using payment vehicle X, and mayascertain that the call may be regarding a payment dispute pertaining tothe online shopping.

In one embodiment, training module 207 may train machine learning module209 using various inputs to enable machine learning module 209 toautomatically find contextual information associated with the users fromunstructured data. In another embodiment, training module 207 may trainmachine learning module 209 using various inputs to identify key data,e.g., descriptive data, supplemental data, etc., related to contextualinformation of the users. In a further embodiment, training module 207may train machine learning module 209 using various inputs to enablemachine learning module 209 to combine unstructured data with structureddata to improve the accuracy of artificial intelligence models, validatedata, and leverage for advisors and customer engagement. In oneinstance, the training module 207 may continuously provide and/or updatemachine learning module 209 during training using, for instance,supervised deep convolution network or equivalents. In one embodiment,training module 207 may include an artificial intelligence (AI) engineconfigured to use natural language processing to conduct automatedconversations with the users. For example, training module 207 mayautomatically process responses and may determine the intentions of thecallers based on the response.

In one embodiment, machine learning module 209 is data-driven and takesinto account different combinations of the data. As can be appreciatedby one skilled in the art, machine learning techniques can be used topredict and improve the potency of the user's data. Machine learning caningest the user's data, draw parallels and conclusions across disparatedata sets to provide refined data. The refined data can then beabstracted further by performing operations such as categorizing,coding, transforming, interpreting, summarizing, and calculating.Further, the abstracted data can be used in the future fordecision-making. In one embodiment, machine learning module 209 may beprovided with IVR interactions data and user profile information by thetraining module 207 during the training phase. Machine learning module209 may analyze the provided data to predict the callers' intent incalling up the organization via the IVR. In another embodiment, machinelearning module 209 may analyze a large corpus of IVR interactions ofcallers to construct a model configured to anticipate and/or predict theservices the callers in the IVR are likely to require. In a furtherembodiment, machine learning module 209 may analyze historical recordsof one or more callers to determine a purpose for the call and provide apredicted service the caller is likely to require. For example, machinelearning module 209 may query database 115 to determine interactionhistory and profile information, or a combination thereof associatedwith the caller to obtain a predicted intent of the caller.

In one embodiment, user interface module 211 may enable a presentationof a graphical user interface (GUI) in a UE 101 associated with a user,e.g., an agent, a caller, etc. User interface module 211 may employvarious application programming interfaces (APIs) or other functioncalls corresponding to the applications on UE 101, thus enabling thedisplay of graphics primitives such as icons, menus, buttons, data entryfields, etc., for generating the user interface elements. In anotherembodiment, user interface module 211 may cause interfacing of theguidance information with the users, e.g., an agent, a caller, etc., toinclude, at least in part, one or more annotations, scripts, textmessages, audio messages, video messages, or a combination thereof.Still further, user interface module 211 may be configured to operate inconnection with augmented reality (AR) processing techniques, whereinvarious applications, graphic elements, and features may interact. Inone example embodiment, integration platform 109 may collaborate withuser interface module 211 to generate a unified display, e.g., a singlepane of glass, by abstracting the behavior of the underlying systemsinto one interface. In another example embodiment, user interface module211 may comprise a variety of interfaces, for example, interfaces fordata input and output devices, referred to as I/O devices, storagedevices, and the like. In a further example embodiment, user interfacemodule 211 may implement a conversational user experience “UX” thatpresents one or more automated interfaces to the callers and learnsabout the callers based on information supplied by the user to theautomated interface. In one embodiment, user interface module 211 mayorganize, automate, and synchronize user information to provide improvedassistance and a personalized user experience. Though data spansmultiple sources and sub-types, user interface module 211 may presentthe data in a simple user interface single user experience.

The above-presented modules and components of integration platform 109may be implemented in hardware, firmware, software, or a combinationthereof. Though depicted as a separate entity in FIG. 1 , it iscontemplated that integration platform 109 may be implemented for directoperation by respective UE 101. As such, integration platform 109 maygenerate direct signal inputs by way of the operating system of the UE101. In another embodiment, one or more of the modules 201-211 may beimplemented for operation by respective UEs, as integration platform109, or a combination thereof. The various executions presented hereincontemplate any and all arrangements and models.

FIG. 3 is a flowchart of a process for real-time processing ofcontextual information of one or more callers to generate dynamiccustomized scripts, according to one embodiment. In various embodiments,integration platform 109 and/or any of modules 201-211 may perform oneor more portions of process 300 and may be implemented in, for instance,a chip set including a processor and a memory as shown in FIG. 12 . Assuch, integration platform 109 and/or any of modules 201-211 may providemeans for accomplishing various parts of process 300, as well as meansfor accomplishing embodiments of other processes described herein inconjunction with other components of system 100. Although process 300 isillustrated and described as a sequence of steps, it is contemplatedthat various embodiments of process 300 may be performed in any order orcombination and need not include all of the illustrated steps.

In step 301, integration platform 109 may receive, via an interactivevoice response (IVR) system, a request from a mobile device associatedwith at least one user. In one example embodiment, users may call, viatheir UE 101, service providers e.g., contact centers, with a request,e.g., issues with certain card transaction, dispute a transaction,various other transaction-related inquiries, etc. The calls may bereceived by IVR system 113 of the service providers. In one exampleembodiment, a call is placed by a caller to a contact center of aservice provider, e.g., a bank. The call may be directed, through thepublic switched telephone network, although, calls or communications mayalso be received through other channels, such as the Internet, privatebranch exchange, intranet VOIP, etc. The source address of the call,e.g., calling telephone number, IP address, or other identifiers, isreceived by integration platform 109 to identify the caller.

In step 303, integration platform 109 may process, in real-time,contextual information associated with at least one user based, at leastin part, on the request. In one embodiment, the contextual informationincludes past interactions with IVR system 113, online activityinformation, preference information, location information, historicalactivity patterns, or a combination thereof associated with at least oneuser. In one example embodiment, as the users are interacting with IVRsystem 113, the integration platform 109 may process contextualinformation of the users based, at least in part, on the userinformation received via IVR system 113. In another example embodiment,while the users are interacting with IVR system 113, the integrationplatform 109 may receive, in real-time, contextual information fromdatabase 115, content provider 117, services platform 119, or acombination thereof. The integration platform 109 may process thecontextual information to determine the intent for the call.

In step 305, integration platform 109 may generate, in real-time, adynamic customized script specific to the request based, at least inpart, on the processing of the contextual information. In oneembodiment, a dynamic customized script is a script that changes inreal-time based, at least in part, on contextual information of theuser, user interaction with IVR system 113, or a combination thereof. Inone example embodiment, sentences in the dynamic customized script maybe dynamically reordered, added, or deleted in real-time for the benefitof the users. Such sentences may be dynamically altered in the script bybeing dynamically reordered or created in real-time, retrieved from arepository of relevant industry and software descriptions, or in otherways as will occur to those of skill in the art. In another embodiment,integration platform 109 may generate any other type of notifications,e.g., aural, visual, etc., in a user interface of a device associatedwith the agents based on the contextual information of the caller.

In step 307, integration platform 109 may route, via IVR system 113, therequest to an agent from a pool of agents of the service provider. Inone embodiment, the agent is selected from the pool of agents based, atleast in part, on the performance level of the agent, the experience ofthe agent, previous interaction of the agent with at least one user,language skills of the agent, or a combination thereof. In anotherembodiment, integration platform 109 may route a caller to an agent viaa matching algorithm. In one example embodiment, the matching algorithmmay match a caller to an agent based on the probability of successduring the interaction. The matching algorithm may also use historicalinteractions of the agents with the users to find patterns for theuser-agent pairs and the result of interactions. In one embodiment,integration platform 109 may implement skill-based routing algorithmsfor selecting at least one agent with the best match of skills to handlethe problem presented.

In step 309, integration platform 109 may generate a presentation of thedynamic customized script in a user interface of a device associatedwith the agent. In one embodiment, the dynamic customized script isstep-by-step guidance to the agent for handling the request of a user.In another embodiment, the dynamic customized script is presented as aunified display in an interface of UE 101 associated with theagent/service provider.

FIG. 4 is a flowchart of a process for updating the dynamic customizedscript, according to one embodiment. In various embodiments, integrationplatform 109 and/or any of modules 201-211 may perform one or moreportions of process 400 and may be implemented in, for instance, a chipset including a processor and a memory as shown in FIG. 12 . As such,integration platform 109 and/or any of modules 201-211 may provide meansfor accomplishing various parts of process 400, as well as means foraccomplishing embodiments of other processes described herein inconjunction with other components of system 100. Although process 400 isillustrated and described as a sequence of steps, it is contemplatedthat various embodiments of process 400 may be performed in any order orcombination and need not include all of the illustrated steps.

In step 401, integration platform 109 may update, in real-time, thedynamic customized script based, at least in part, on a response of atleast one user. In one embodiment, the response includes userinteractions with IVR system 113, user interactions with the agents, ora combination thereof. In another embodiment, integration platform 109may update, in real-time, the dynamic customized script based, at leastin part, on contextual information of the user.

In step 403, integration platform 109 may generate a presentation of theupdated dynamic customized script in the user interface of the deviceassociated with the agent. In one embodiment, integration platform 109may present the updated dynamic customized script in real-time.

FIG. 5 is a flowchart of a process for determining authenticationmechanisms for a user, according to one embodiment. In variousembodiments, integration platform 109 and/or any of modules 201-211 mayperform one or more portions of process 500 and may be implemented in,for instance, a chip set including a processor and a memory as shown inFIG. 12 . As such, integration platform 109 and/or any of modules201-211 may provide means for accomplishing various parts of process500, as well as means for accomplishing embodiments of other processesdescribed herein in conjunction with other components of system 100.Although process 500 is illustrated and described as a sequence ofsteps, it is contemplated that various embodiments of process 500 may beperformed in any order or combination and need not include all of theillustrated steps.

In step 501, integration platform 109 may compare profile information ofat least one user to at least one stored user information in a database.In one embodiment, the profile information may include name information,age information, gender information, behavior information, preferenceinformation, purchase history information, location information, deviceinformation, or a combination thereof associated with the user. In oneexample embodiment, integration platform 109 may compare nameinformation and device information of the caller with the stored nameinformation and device information in database 115 to determine a match.In another example embodiment, integration platform 109 may comparepurchase history information and location information with the storedpurchase history information and location information in database 115 todetermine a match.

In step 503, integration platform 109 may determine a confidence levelbased, at least in part, on the comparison of the profile information tothe stored user information. In one example embodiment, integrationplatform 109 may determine a high confidence level upon determining amatch between the profile information to the stored user information. Inanother example embodiment, integration platform 109 may determine a lowconfidence level upon determining incompatibility between the profileinformation to the stored user information.

In step 505, integration platform 109 may determine one or moreauthentication mechanisms for at least one user based, at least in part,on the confidence level. In one embodiment, the one or moreauthentication mechanisms include predefined values, a preset usernameand password, user identification, device identification, a biometricpattern corresponding to the user, or a combination thereof. In oneembodiment, the authentication level increases with a decrease in theconfidence level and vice-versa. In one example embodiment, integrationplatform 109 may perform multiple-step authentication of the user upondetermining a low confidence level. In another example embodiment,integration platform 109 may perform a simple authentication of theuser, e.g., username and password verification, upon determining a highconfidence level. During the identity confirmation process, the callermay be directed to provide certain details relating to the intent of thecall. For example, the call may be asked to “press one for sales, twofor service, three for technical support, four for returns, and five forother”. Each selected choice, for example, could include a further menu,or an interactive voice response, or an option to record information.

In step 507, integration platform 109 may authenticate, via acomputer-telephony integration (CTI) Application Programming Interface(API), the at least one user. In one embodiment, data pertaining to therequest is transmitted to the CTI API during the authentication, whereinthe data includes payload information.

In step 509, integration platform 109 may calculate, via the CTI API, aunique key based, at least in part, on the data. The CTI system 111 oranother information processing device operable in cooperation with theCTI system 111 may generate a unique key. In one example embodiment, theunique key is a user session ID. In another example embodiment, theunique key is a specific customer record. In one example embodiment, thecaller may have privacy concerns while sharing confidential information.Integration platform 109 may anonymize the information that is sent bythe callers to authentication module 203. In one embodiment, integrationplatform 109 may encode the confidential information shared by thecaller. For example, instead of transmitting the phone numbers, anencoding of the phone number may be transmitted. The encoding may beperformed through a predetermined algorithm. Such encoding algorithmswould allow for the one-way encoding of information and not allow forthe encoded information to be decoded, such examples of encoding arehashes.

In step 511, integration platform 109 may transmit, via the CTI API, theunique key to a session initiation protocol (SIP) header. In one exampleembodiment, the unique key is transmitted to both UE 101 and the serviceprovider. The user inputs the unique key to UE 101 for authenticatingaccess to the service provider. The integration platform 109 comparesthe unique key and allows access to the service upon match. Then, theunique key which has been used for the authentication is invalidated.

FIG. 6 is a flowchart of a process for generating a dynamic customizedscript based on the current activity of the user, according to oneembodiment. In various embodiments, integration platform 109 and/or anyof modules 201-211 may perform one or more portions of process 600 andmay be implemented in, for instance, a chip set including a processorand a memory as shown in FIG. 12 . As such, integration platform 109and/or any of modules 201-211 may provide means for accomplishingvarious parts of process 600, as well as means for accomplishingembodiments of other processes described herein in conjunction withother components of system 100. Although process 600 is illustrated anddescribed as a sequence of steps, it is contemplated that variousembodiments of process 600 may be performed in any order or combinationand need not include all of the illustrated steps.

In step 601, integration platform 109 may process, in real-time, anatural language used by at least one user during user interaction withthe IVR system, the online activity information, the locationinformation, or a combination thereof to determine the current activityof the at least one user. In one example embodiment, integrationplatform 109 may determine the user is accessing a web portal to doonline shopping from his residence via sensor 105. The integrationplatform 109 may process the words uttered by the caller during the callto determine that the call is related to online shopping.

In step 603, integration platform 109 may generate, in real-time, thedynamic customized script based, at least in part, on the currentactivity of at least one user. In one embodiment, integration platform109 may generate a dynamic script that is customized to address all thequeries relating to the callers' online transition. For example, thedynamic script includes the types of questions to be asked to the callerabout the online transaction, the answers to the questions that might beasked by the caller, or a combination thereof. These questions andanswers may be updated in real-time based on the response of the caller.

FIG. 7 is a flowchart of a process for presenting a dynamic customizedscript, according to one embodiment. In various embodiments, integrationplatform 109 and/or any of modules 201-211 may perform one or moreportions of process 700 and may be implemented in, for instance, a chipset including a processor and a memory as shown in FIG. 12 . As such,integration platform 109 and/or any of modules 201-211 may provide meansfor accomplishing various parts of process 700, as well as means foraccomplishing embodiments of other processes described herein inconjunction with other components of system 100. Although process 700 isillustrated and described as a sequence of steps, it is contemplatedthat various embodiments of process 700 may be performed in any order orcombination and need not include all of the illustrated steps.

In step 701, integration platform 109 may generate a user interface in adevice associated with the agent comprising a first area to display datarelated to at least one user. In one embodiment, the data includes userinformation, credit card information, transaction history information,account benefit information, address information, call historyinformation, or a combination thereof.

In step 703, integration platform 109 may generate a user interface in adevice associated with the agent comprising a second area adjacent tothe first area to display the dynamic customized script. In oneembodiment, the dynamic customized script includes a prompt generator togenerate an audio notification, a visual notification, a textualnotification, or a combination thereof.

FIG. 8 is a flowchart of a process for predicting the intent of a call,according to one embodiment. In various embodiments, integrationplatform 109 and/or any of modules 201-211 may perform one or moreportions of process 800 and may be implemented in, for instance, a chipset including a processor and a memory as shown in FIG. 12 . As such,integration platform 109 and/or any of modules 201-211 may provide meansfor accomplishing various parts of process 800, as well as means foraccomplishing embodiments of other processes described herein inconjunction with other components of system 100. Although process 800 isillustrated and described as a sequence of steps, it is contemplatedthat various embodiments of process 800 may be performed in any order orcombination and need not include all of the illustrated steps.

In step 801, integration platform 109 may generate a plurality ofpredictions about a reason for the request based, at least in part, onthe processing of the contextual information. In one example embodiment,integration platform 109 may predict the caller is calling to dispute atransaction based on historical information of the caller. In oneexample embodiment, integration platform 109 may predict the caller iscalling regarding an online transaction based on the current activity ofthe caller. In one example embodiment, integration platform 109 maypredict the caller is calling to file a complaint based on the tone ofthe user while interacting with IVR system 113.

In step 803, integration platform 109 may compute a probability scorefor the plurality of predictions and may determine at least oneprediction with the highest probability score from the plurality ofpredictions. In one embodiment, the highest probability score exceeds aprobability score threshold. In one example embodiment, the integrationplatform 109 may compute a probability score of 72% that the caller iscalling to dispute a transaction and a probability score of 80% that thecaller is calling to file a complaint. The integration platform 109 maychoose to determine that the caller is calling to file a complaint basedon the probability score.

In step 805, integration platform 109 may input at least one predictioninto the dynamic customized script. In one example embodiment,integration platform 109 may generate a dynamic script that iscustomized to address all the questions of the caller to resolve theissue and prevent the caller from filing a complaint.

FIGS. 9A-C illustrate agent interface screens, according to variousexample embodiments. In one embodiment, integration platform 109 maypresent data from multiple sources in a unified display, i.e., a singlepane of glass, by abstracting the behavior of the underlying systemsinto one interface. The agent sees one interface that consolidates allunderlying backend systems, so they do not need to work with disparatesystems or navigation constructs. This is helpful for the agents as theydo not have to switch between multiple applications and have a uniformmethod of interaction in all scenarios. This also reduces thepossibility of errors because there is a single point of entry.

In one embodiment, integration platform 109 may generate a dynamicscript based on the data received, e.g., the nature of the incomingcall, from the IVR. For example, a dispute will be treated differentlyfrom reporting a stolen card. In one embodiment, there is flexibilitywithin the system for the administrators to configure, in real-time, theflow dynamically. This is important particularly in areas where thereare regulatory norms that can change the flow. For example, specificbusinesses have service level agreements (SLA), e.g., a set ofinternally-determined standards to guide decisions, tied to calldurations.

As depicted in FIG. 9A, agent 901 receives a dynamic customized scriptin user interface 903. User interface 903 comprises first area 905 todisplay data related to the caller. The data includes user information,credit card information, transaction history information, accountbenefit information, address information, call history information, or acombination thereof. User interface 903 also comprises second area 907for presenting the dynamic customized script and additional informationrelating to the call. In one embodiment, the second area 907 includes aprompt generator to generate an audio notification, a visualnotification, textual notification, or a combination thereof to assistthe agent/service provider. It is understood that user interface 903 maybe in any other format as desired.

In one embodiment, agent 901 interacts with the callers by followingdynamic script 909. The dynamic script is updated based on thecontextual information of the caller, the response of the caller whileinteracting with IVR system 113, or a combination thereof. In oneembodiment, integration platform 109 may detect, in real-time, theprogression of the interaction between the caller and the agent and mayautomatically navigate the dynamic scripts for the agent, e.g., scripts911 and then 913 are presented in the interface as the agent finishesthe last question of the script or reaches the end of the dynamic script(FIGS. 9B and 9C). In one embodiment, integration platform 109 maynavigate the dynamic scripts based, at least in part, on the speed ofthe conversation. In one example embodiment, integration platform 109may navigate to scripts 911 and 913 based, at least in part on, thepredicted time threshold.

FIG. 10 is a flowchart for integration of CTI system 111 and IVR system113 deployed within a contact center by integration platform 109,according to one example embodiment. In one embodiment, the IVR systemmay prompt callers to authenticate themselves, and also enter additionaldetails, e.g., the purpose of the call. During this authentication, ifthe caller needs to speak with an agent, the caller may select the IVRoption that performs the call transfer to an agent.

While the call transfer is being processed, a web service API call ismade into a CTI REPO RestAPI 1001 which performs OAuth2.0authentication. During this API call, specific details regarding thecall, e.g., payload information, caller, e.g., contextual information,and purpose of the call may be passed. These data are written into anAWS Elastic Cache data store1003, thereafter a unique key to the data iscalculated, e.g., token, and returned by the CTI REPO RestAPI 1001service to IVR 1005.

During a session initiation protocol (SIP) based call transfer of thecaller to the call center, the calculated token is passed into theuser-to-user (UUI) field of SIP header 1007. When the call arrives atthe call center, integration platform 109 may perform a normal calldistribution processing and may select an agent. As the call arrives atthe agent's desktop, the call center SIP call handler delivers the tokento the Agent OnePage App and Call Scripter 1009. This token is then usedby Agent OnePage App and Call Scripter 1009 to retrieve informationabout the call from AWS Elastic Cache data store 1003. Specific detailsregarding the call are then presented to the agent with thecaller-specific agent script.

One or more implementations disclosed herein include and/or may beimplemented using a machine learning model. For example, one or more ofthe speech recognition algorithms, initialization algorithm,machine-learning algorithms, matching algorithm, skill-based routingalgorithms, and encoding algorithms may be implemented using a machinelearning model and/or may be used to train a machine learning model. Agiven machine learning model may be trained using the data flow 1110 ofFIG. 11 . Training data 1112 may include one or more of stage inputs1114 and known outcomes 1118 related to a machine learning model to betrained. The stage inputs 1114 may be from any applicable sourceincluding text, visual representations, data, values, comparisons, stageoutputs (e.g., one or more outputs from a step from FIGS. 3, 4, 5, 6, 7, and/or 8). The known outcomes 1118 may be included for machinelearning models generated based on supervised or semi-supervisedtraining. An unsupervised machine learning model may not be trainedusing known outcomes 1118. Known outcomes 1118 may include known ordesired outputs for future inputs similar to or in the same category asstage inputs 1114 that do not have corresponding known outputs.

The training data 1112 and a training algorithm 1120 (e.g., one or moreof the speech recognition algorithms, initialization algorithm,machine-learning algorithms, matching algorithm, skill-based routingalgorithms, and encoding algorithms implemented using a machine learningmodel and/or may be used to train a machine learning model) may beprovided to a training component 1130 that may apply the training data1112 to the training algorithm 1120 to generate a machine learningmodel. According to an implementation, the training component 1130 maybe provided comparison results 1116 that compare a previous output ofthe corresponding machine learning model to apply the previous result tore-train the machine learning model. The comparison results 1116 may beused by the training component 1130 to update the corresponding machinelearning model. The training algorithm 1120 may utilize machine learningnetworks and/or models including, but not limited to a deep learningnetwork such as Deep Neural Networks (DNN), Convolutional NeuralNetworks (CNN), Fully Convolutional Networks (FCN) and Recurrent NeuralNetworks (RCN), probabilistic models such as Bayesian Networks andGraphical Models, and/or discriminative models such as Decision Forestsand maximum margin methods, or the like.

A machine learning model used herein may be trained and/or used byadjusting one or more weights and/or one or more layers of the machinelearning model. For example, during training, a given weight may beadjusted (e.g., increased, decreased, removed) based on training data orinput data. Similarly, a layer may be updated, added, or removed basedon training data/and or input data. The resulting outputs may beadjusted based on the adjusted weights and/or layers.

In general, any process or operation discussed in this disclosure thatis understood to be computer-implementable, such as the processillustrated in FIGS. 3, 4, 5, 6, 7 , and/or 8 may be performed by one ormore processors of a computer system as described herein. A process orprocess step performed by one or more processors may also be referred toas an operation. The one or more processors may be configured to performsuch processes by having access to instructions (e.g., software orcomputer-readable code) that, when executed by the one or moreprocessors, cause the one or more processors to perform the processes.The instructions may be stored in a memory of the computer system. Aprocessor may be a central processing unit (CPU), a graphics processingunit (GPU), or any suitable types of processing unit.

A computer system, such as a system or device implementing a process oroperation in the examples above, may include one or more computingdevices. One or more processors of a computer system may be included ina single computing device or distributed among a plurality of computingdevices. One or more processors of a computer system may be connected toa data storage device. A memory of the computer system may include therespective memory of each computing device of the plurality of computingdevices.

In various embodiments, one or more portions of processes 300, 400, 500,600, 700, and 800 may be implemented in, for instance, a chip setincluding a processor and a memory as shown in FIG. 8 . FIG. 12illustrates an implementation of a general computer system that mayexecute techniques presented herein. The computer system 1200 caninclude a set of instructions that can be executed to cause the computersystem 1200 to perform any one or more of the methods, system, orcomputer based functions disclosed herein. The computer system 1200 mayoperate as a standalone device or may be connected, e.g., using anetwork, to other computer systems or peripheral devices.

Unless specifically stated otherwise, as apparent from the followingdiscussions, it is appreciated that throughout the specification,discussions utilizing terms such as “processing,” “computing,”“calculating,” “determining”, analyzing” or the like, refer to theaction and/or processes of a computer or computing system, or similarelectronic computing device, that manipulate and/or transform datarepresented as physical, such as electronic, quantities into other datasimilarly represented as physical quantities.

In a similar manner, the term “processor” may refer to any device orportion of a device that processes electronic data, e.g., from registersand/or memory to transform that electronic data into other electronicdata that, e.g., may be stored in registers and/or memory. A “computer,”a “computing machine,” a “computing platform,” a “computing device,” ora “server” may include one or more processors.

In a networked deployment, the computer system 1200 may operate in thecapacity of a server or as a client user computer in a server-clientuser network environment, or as a peer computer system in a peer-to-peer(or distributed) network environment. The computer system 1200 can alsobe implemented as or incorporated into various devices, such as apersonal computer (PC), a tablet PC, a personal digital assistant (PDA),a mobile device, a palmtop computer, a laptop computer, a desktopcomputer, a communications device, a wireless telephone, a land-linetelephone, a control system, a camera, a scanner, a facsimile machine, apersonal trusted device, a web appliance, a network router, switch orbridge, or any other machine capable of executing a set of instructions(sequential or otherwise) that specify actions to be taken by thatmachine. In a particular implementation, the computer system 1200 can beimplemented using electronic devices that provide voice, video, or datacommunication. Further, while a computer system 1200 is illustrated as asingle system, the term “system” shall also be taken to include anycollection of systems or sub-systems that individually or jointlyexecute a set, or multiple sets, of instructions to perform one or morecomputer functions.

As illustrated in FIG. 12 , the computer system 1200 may include aprocessor 1202, e.g., a central processing unit (CPU), a graphicsprocessing unit (GPU), or both. The processor 1202 may be a component ina variety of systems. For example, the processor 1202 may be part of astandard personal computer or a workstation. The processor 1202 may beone or more general processors, digital signal processors, applicationspecific integrated circuits, field programmable gate arrays, servers,networks, digital circuits, analog circuits, combinations thereof, orother now known or later developed devices for analyzing and processingdata. The processor 1202 may implement a software program, such as codegenerated manually (i.e., programmed).

The computer system 1200 may include a memory 1204 that can communicatevia a bus 1208. The memory 1204 may be a main memory, a static memory,or a dynamic memory. The memory 1204 may include, but is not limited tocomputer readable storage media such as various types of volatile andnon-volatile storage media, including but not limited to random accessmemory, read-only memory, programmable read-only memory, electricallyprogrammable read-only memory, electrically erasable read-only memory,flash memory, magnetic tape or disk, optical media and the like. In oneimplementation, the memory 1204 includes a cache or random-access memoryfor the processor 1202. In alternative implementations, the memory 1204is separate from the processor 1202, such as a cache memory of aprocessor, the system memory, or other memory. The memory 1204 may be anexternal storage device or database for storing data. Examples include ahard drive, corn pact disc (“CD”), digital video disc (“DVD”), memorycard, memory stick, floppy disc, universal serial bus (“USB”) memorydevice, or any other device operative to store data. The memory 1204 isoperable to store instructions executable by the processor 1202. Thefunctions, acts or tasks illustrated in the figures or described hereinmay be performed by the processor 1202 executing the instructions storedin the memory 1204. The functions, acts or tasks are independent of theparticular type of instructions set, storage media, processor orprocessing strategy and may be performed by software, hardware,integrated circuits, firm-ware, micro-code and the like, operating aloneor in combination. Likewise, processing strategies may includemultiprocessing, multitasking, parallel processing and the like.

As shown, the computer system 1200 may further include a display 1210,such as a liquid crystal display (LCD), an organic light emitting diode(OLED), a flat panel display, a solid-state display, a cathode ray tube(CRT), a projector, a printer or other now known or later developeddisplay device for outputting determined information. The display 1210may act as an interface for the user to see the functioning of theprocessor 1202, or specifically as an interface with the software storedin the memory 1204 or in the drive unit 1206.

Additionally or alternatively, the computer system 1200 may include aninput/output device 1212 configured to allow a user to interact with anyof the components of computer system 1200. The input/output device 1212may be a number pad, a keyboard, or a cursor control device, such as amouse, or a joystick, touch screen display, remote control, or any otherdevice operative to interact with the computer system 1200.

The computer system 1200 may also or alternatively include drive unit1206 implemented as a disk or optical drive. The drive unit 1206 mayinclude a computer-readable medium 1222 in which one or more sets ofinstructions 1224, e.g. software, can be embedded. Further, instructions1224 may embody one or more of the methods or logic as described herein.The instructions 1224 may reside completely or partially within thememory 1204 and/or within the processor 1202 during execution by thecomputer system 1200. The memory 1204 and the processor 1202 also mayinclude computer-readable media as discussed above.

In some systems, a computer-readable medium 1222 includes instructions1224 or receives and executes instructions 1224 responsive to apropagated signal so that a device connected to a network 1270 cancommunicate voice, video, audio, images, or any other data over thenetwork 1270. Further, the instructions 1224 may be transmitted orreceived over the network 1270 via a communication port or interface1220, and/or using a bus 1208. The communication port or interface 1220may be a part of the processor 1202 or may be a separate component. Thecommunication port or interface 1220 may be created in software or maybe a physical connection in hardware. The communication port orinterface 1220 may be configured to connect with a network 1270,external media, the display 1210, or any other components in computersystem 1200, or combinations thereof. The connection with the network1270 may be a physical connection, such as a wired Ethernet connectionor may be established wirelessly as discussed below. Likewise, theadditional connections with other components of the computer system 1200may be physical connections or may be established wirelessly. Thenetwork 1270 may alternatively be directly connected to a bus 1208.

While the computer-readable medium 1222 is shown to be a single medium,the term “computer-readable medium” may include a single medium ormultiple media, such as a centralized or distributed database, and/orassociated caches and servers that store one or more sets ofinstructions. The term “computer-readable medium” may also include anymedium that is capable of storing, encoding, or carrying a set ofinstructions for execution by a processor or that cause a computersystem to perform any one or more of the methods or operations disclosedherein. The computer-readable medium 1222 may be non-transitory, and maybe tangible.

The computer-readable medium 1222 can include a solid-state memory suchas a memory card or other package that houses one or more non-volatileread-only memories. The computer-readable medium 1222 can be arandom-access memory or other volatile re-writable memory. Additionallyor alternatively, the computer-readable medium 1222 can include amagneto-optical or optical medium, such as a disk or tapes or otherstorage device to capture carrier wave signals such as a signalcommunicated over a transmission medium. A digital file attachment to ane-mail or other self-contained information archive or set of archivesmay be considered a distribution medium that is a tangible storagemedium. Accordingly, the disclosure is considered to include any one ormore of a computer-readable medium or a distribution medium and otherequivalents and successor media, in which data or instructions may bestored.

In an alternative implementation, dedicated hardware implementations,such as application specific integrated circuits, programmable logicarrays and other hardware devices, can be constructed to implement oneor more of the methods described herein. Applications that may includethe apparatus and systems of various implementations can broadly includea variety of electronic and computer systems. One or moreimplementations described herein may implement functions using two ormore specific interconnected hardware modules or devices with relatedcontrol and data signals that can be communicated between and throughthe modules, or as portions of an application-specific integratedcircuit. Accordingly, the present system encompasses software, firmware,and hardware implementations.

The computer system 1200 may be connected to a network 1270. The network1270 may define one or more networks including wired or wirelessnetworks. The wireless network may be a cellular telephone network, an802.11, 802.16, 802.20, or WiMAX network. Further, such networks mayinclude a public network, such as the Internet, a private network, suchas an intranet, or combinations thereof, and may utilize a variety ofnetworking protocols now available or later developed including, but notlimited to TCP/IP based networking protocols. The network 1270 mayinclude wide area networks (WAN), such as the Internet, local areanetworks (LAN), campus area networks, metropolitan area networks, adirect connection such as through a Universal Serial Bus (USB) port, orany other networks that may allow for data communication. The network1270 may be configured to couple one computing device to anothercomputing device to enable communication of data between the devices.The network 1270 may generally be enabled to employ any form ofmachine-readable media for communicating information from one device toanother. The network 1270 may include communication methods by whichinformation may travel between computing devices. The network 1270 maybe divided into sub-networks. The sub-networks may allow access to allof the other components connected thereto or the sub-networks mayrestrict access between the components. The network 1270 may be regardedas a public or private network connection and may include, for example,a virtual private network or an encryption or other security mechanismemployed over the public Internet, or the like.

In accordance with various implementations of the present disclosure,the methods described herein may be implemented by software programsexecutable by a computer system. Further, in an exemplary, non-limitedimplementation, implementations can include distributed processing,component/object distributed processing, and parallel processing.Alternatively, virtual computer system processing can be constructed toimplement one or more of the methods or functionality as describedherein.

Although the present specification describes components and functionsthat may be implemented in particular implementations with reference toparticular standards and protocols, the disclosure is not limited tosuch standards and protocols. For example, standards for Internet andother packet switched network transmission (e.g., TCP/IP, UDP/IP, HTML,HTTP) represent examples of the state of the art. Such standards areperiodically superseded by faster or more efficient equivalents havingessentially the same functions. Accordingly, replacement standards andprotocols having the same or similar functions as those disclosed hereinare considered equivalents thereof.

It will be understood that the steps of methods discussed are performedin one embodiment by an appropriate processor (or processors) of aprocessing (i.e., computer) system executing instructions(computer-readable code) stored in storage. It will also be understoodthat the disclosure is not limited to any particular implementation orprogramming technique and that the disclosure may be implemented usingany appropriate techniques for implementing the functionality describedherein. The disclosure is not limited to any particular programminglanguage or operating system.

It should be appreciated that in the above description of exemplaryembodiments of the invention, various features of the invention aresometimes grouped together in a single embodiment, figure, ordescription thereof for the purpose of streamlining the disclosure andaiding in the understanding of one or more of the various inventiveaspects. This method of disclosure, however, is not to be interpreted asreflecting an intention that the claimed invention requires morefeatures than are expressly recited in each claim. Rather, as thefollowing claims reflect, inventive aspects lie in less than allfeatures of a single foregoing disclosed embodiment. Thus, the claimsfollowing the Detailed Description are hereby expressly incorporatedinto this Detailed Description, with each claim standing on its own as aseparate embodiment of this invention.

Furthermore, while some embodiments described herein include some butnot other features included in other embodiments, combinations offeatures of different embodiments are meant to be within the scope ofthe invention, and form different embodiments, as would be understood bythose skilled in the art. For example, in the following claims, any ofthe claimed embodiments can be used in any combination.

Furthermore, some of the embodiments are described herein as a method orcombination of elements of a method that can be implemented by aprocessor of a computer system or by other means of carrying out thefunction. Thus, a processor with the necessary instructions for carryingout such a method or element of a method forms a means for carrying outthe method or element of a method. Furthermore, an element describedherein of an apparatus embodiment is an example of a means for carryingout the function performed by the element for the purpose of carryingout the invention.

In the description provided herein, numerous specific details are setforth. However, it is understood that embodiments of the invention maybe practiced without these specific details. In other instances,well-known methods, structures and techniques have not been shown indetail in order not to obscure an understanding of this description.

Thus, while there has been described what are believed to be thepreferred embodiments of the invention, those skilled in the art willrecognize that other and further modifications may be made theretowithout departing from the spirit of the invention, and it is intendedto claim all such changes and modifications as falling within the scopeof the invention. For example, any formulas given above are merelyrepresentative of procedures that may be used. Functionality may beadded or deleted from the block diagrams and operations may beinterchanged among functional blocks. Steps may be added or deleted tomethods described within the scope of the present invention.

The above disclosed subject matter is to be considered illustrative, andnot restrictive, and the appended claims are intended to cover all suchmodifications, enhancements, and other implementations, which fallwithin the true spirit and scope of the present disclosure. Thus, to themaximum extent allowed by law, the scope of the present disclosure is tobe determined by the broadest permissible interpretation of thefollowing claims and their equivalents, and shall not be restricted orlimited by the foregoing detailed description. While variousimplementations of the disclosure have been described, it will beapparent to those of ordinary skill in the art that many moreimplementations and implementations are possible within the scope of thedisclosure. Accordingly, the disclosure is not to be restricted exceptin light of the attached claims and their equivalents.

1-20. (canceled)
 21. A system for generating a dynamic customized scriptto facilitate communication, the system comprising: one or moreprocessors; a data storage comprising instructions which, when executedby the one or more processors, cause the one or more processors toperform operations comprising: receiving, by an integration platformutilizing an interactive voice response (IVR) system, a call requestfrom a device associated with a user; processing, by the integrationplatform utilizing the IVR system, the call request to determinecontextual information and generate the dynamic customized script;transmitting, by the integration platform utilizing the IVR system, thecall request to a computer-telephony integration (CTI) system for userauthentication and call routing; routing, by the integration platformutilizing the CTI system, the call request to an agent; and generating,by the integration platform, a presentation of the dynamic customizedscript in a user interface of a device associated with the agent. 22.The system of claim 21, wherein processing the call request to determinethe contextual information, comprises: collecting, by the integrationplatform, relevant data associated with the user via one or more datacollection techniques, wherein the relevant data includes activityinformation, historical information, preference information, or acombination thereof; and processing, by the integration platformutilizing a machine learning model, the relevant data and one or moreresponses of the user to predict an intention for the call request. 23.The system of claim 21, wherein the operations further comprise:updating, by the integration platform, the dynamic customized scriptbased on interaction of the user with the IVR system, interaction of theuser with the agent, or a combination thereof.
 24. The system of claim21, wherein the user authentication comprises: validating, by theintegration platform utilizing the CTI system, the user based on usercredentials, device identifiers, biometrics data, credit cardinformation, or a combination thereof.
 25. The system of claim 24,wherein the operations further comprise: determining, by the integrationplatform, one or more authentication mechanisms to validate the userbased on confidence level, wherein an authentication level increaseswith a decrease in the confidence level.
 26. The system of claim 21,wherein routing the call request to the agent, comprises: routing, bythe integration platform utilizing a matching algorithm, the callrequest to the agent based on probability of success during previousinteractions between the user and one or more agents to find auser-agent pair; or routing, by the integration platform utilizing askill-based routing algorithms, the call request to the agent based onperformance level, experience level, language skills, or a combinationthereof of the agent to handle the call request.
 27. The system of claim22, wherein the IVR system includes voice recognition technology toprocess natural language input from the user for generating the dynamiccustomized script and routing the call request to the agent.
 28. Thesystem of claim 21, the user authentication comprises: transmitting, bythe integration platform, data associated with the call request to theCTI system, wherein the data includes payload information; andcalculating, by the integration platform utilizing the CTI system, aunique key based on the data, wherein the unique key is a sessionidentification (ID), a specific customer record, or a combinationthereof.
 29. The system of claim 28, wherein transmitting, by theintegration platform utilizing the CTI system, the unique key to asession initiation protocol (SIP) header, wherein the unique key isutilized for authenticating an access to a service.
 30. The system ofclaim 21, wherein the dynamic customized script includes a promptgenerator to generate an audio notification, a visual notification, atextual notification, or a combination thereof.
 31. Acomputer-implemented method for generating a dynamic customized scriptto facilitate communication, the method comprising: receiving, by anintegration platform utilizing an interactive voice response (IVR)system, a call request from a device associated with a user; processing,by the integration platform utilizing the IVR system, the call requestto determine contextual information and generate the dynamic customizedscript; transmitting, by the integration platform utilizing the IVRsystem, the call request to a computer-telephony integration (CTI)system for user authentication and call routing; routing, by theintegration platform utilizing the CTI system, the call request to anagent; and generating, by the integration platform, a presentation ofthe dynamic customized script in a user interface of a device associatedwith the agent.
 32. The computer-implemented method of claim 31, whereinprocessing the call request to determine the contextual information,comprises: collecting, by the integration platform, relevant dataassociated with the user via one or more data collection techniques,wherein the relevant data includes activity information, historicalinformation, preference information, or a combination thereof; andprocessing, by the integration platform utilizing a machine learningmodel, the relevant data and one or more responses of the user topredict an intention for the call request.
 33. The computer-implementedmethod of claim 31, the method further comprising: updating, by theintegration platform, the dynamic customized script based on interactionof the user with the IVR system, interaction of the user with the agent,or a combination thereof.
 34. The computer-implemented method of claim31, wherein the user authentication comprises: validating, by theintegration platform utilizing the CTI system, the user based on usercredentials, device identifiers, biometrics data, credit cardinformation, or a combination thereof.
 35. The computer-implementedmethod of claim 34, the method further comprising: determining, by theintegration platform, one or more authentication mechanisms to validatethe user based on confidence level, wherein an authentication levelincreases with a decrease in the confidence level.
 36. Thecomputer-implemented method of claim 31, wherein routing the callrequest to the agent, comprises: routing, by the integration platformutilizing a matching algorithm, the call request to the agent based onprobability of success during previous interactions between the user andone or more agents to find a user-agent pair; or routing, by theintegration platform utilizing a skill-based routing algorithms, thecall request to the agent based on performance level, experience level,language skills, or a combination thereof of the agent to handle thecall request.
 37. The computer-implemented method of claim 31, whereinthe IVR system includes voice recognition technology to process naturallanguage input from the user for generating the dynamic customizedscript and routing the call request to the agent.
 38. A non-transitorycomputer-readable medium storing instructions for generating a dynamiccustomized script to facilitate communication, the instructions, whenexecuted by one or more processors, causing the one or more processorsto perform operations comprising: receiving, by an integration platformutilizing an interactive voice response (IVR) system, a call requestfrom a device associated with a user; processing, by the integrationplatform utilizing the IVR system, the call request to determinecontextual information and generate the dynamic customized script;transmitting, by the integration platform utilizing the IVR system, thecall request to a computer-telephony integration (CTI) system for userauthentication and call routing; routing, by the integration platformutilizing the CTI system, the call request to an agent; and generating,by the integration platform, a presentation of the dynamic customizedscript in a user interface of a device associated with the agent. 39.The computer-readable medium of claim 38, wherein processing the callrequest to determine the contextual information, comprises: collecting,by the integration platform, relevant data associated with the user viaone or more data collection techniques, wherein the relevant dataincludes activity information, historical information, preferenceinformation, or a combination thereof; and processing, by theintegration platform utilizing a machine learning model, the relevantdata and one or more responses of the user to predict an intention forthe call request.
 40. The computer-readable medium of claim 38, theoperations further comprising: updating, by the integration platform,the dynamic customized script based on interaction of the user with theIVR system, interaction of the user with the agent, or a combinationthereof.